Real-time-simulation

说明：  雷达目标跟踪中的直观法航迹起始程序，其中包含两个文件：simutrack.m和main.m。simutrack.m是目标轨迹仿真；main.m是主文件。(matlab code for track initialization in radar target tracking.)

Dijkstra算法的MATLAB实现(Dijkstra algorithm in MATLAB)

数值分析，对于普通的编程实现一定的算法有很大的帮组，这是本人作业，服役偶偶程序计一定的解释，希望能帮助到大家(In order to improve the adaptation property of the system by making the best of the direction of distribution of radar measurement errors, a optimum estimation algorithm for Kepler orbit is proposed, which yields a maximum likelihood estimation of the Kepler orbit with multi-spatio-temporal state based on both the preprocessing of the small manoeuvring model and the determining of initial value parameter with two spatio-temporal state. )

滤波器高斯噪声信道模型和jake s信道模型上的二进制相位调制，matlab实现源码(bpsk on the filtered gaussion noise channel model and jake s model ,matlab code.)

有详细的注释，数值分析的EULER法，基于kaiser窗的双谱线插值FFT谐波分析，采用波束成形技术的BER计算，包括脚本文件和函数文件形式，是学习PCA特征提取的很好的学习资料，非归零型差分相位调制信号建模与仿真分析 。( There are detailed notes, EULER numerical analysis method, Dual-line interpolation FFT harmonic analysis kaiser windows, By applying the beam forming technology of BER Including script files and function files in the form, Is a good learning materials to learn PCA feature extraction, NRZ type differential phase modulation signal modeling and simulation analysis.)

IEEE30节点测试系统matlab M文件，包含各节点信息。。。。(The IEEE30 nodes test system M-file)

增量式PID控制算法（m文件实现，包括三种信号源以及结果的图像展示）(Incremental PID control algorithm (m file implementation, including three kinds of image signal source and the results show))

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)

实现数字波束形成性能仿真分析，可以激情享用。(To attain the digital beamforming performance, I wish you would enlarge the program.)

genetic algorithm for fetcher selection